%0 Journal Article
%A Zhang, Kevin Sun
%A Neelsen, Christian Jan Oliver
%A Wennmann, Markus
%A Hielscher, Thomas
%A Kovacs, Balint
%A Glemser, Philip Alexander
%A Görtz, Magdalena
%A Stenzinger, Albrecht
%A Maier-Hein, Klaus H
%A Huber, Johannes
%A Schlemmer, Heinz-Peter
%A Bonekamp, David
%T In vivo variability of MRI radiomics features in prostate lesions assessed by a test-retest study with repositioning.
%J Scientific reports
%V 15
%N 1
%@ 2045-2322
%C [London]
%I Springer Nature
%M DKFZ-2025-01689
%P 29703
%D 2025
%Z #EA:E010#LA:E010#
%X Despite academic success, radiomics-based machine learning algorithms have not reached clinical practice, partially due to limited repeatability/reproducibility. To address this issue, this work aims to identify a stable subset of radiomics features in prostate MRI for radiomics modelling. A prospective study was conducted in 43 patients who received a clinical MRI examination and a research exam with repetition of T2-weighted and two different diffusion-weighted imaging (DWI) sequences with repositioning in between. Radiomics feature (RF) extraction was performed from MRI segmentations accounting for intra-rater and inter-rater effects, and three different image normalization methods were compared. Stability of RFs was assessed using the concordance correlation coefficient (CCC) for different comparisons: rater effects, inter-scan (before and after repositioning) and inter-sequence (between the two diffusion-weighted sequences) variability. In total, only 64 out of 321 (  20
%K Magnetic resonance imaging (Other)
%K Observer variation (Other)
%K Prostate (Other)
%K Radiomics (Other)
%K Reproducibility of results (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:40804076
%R 10.1038/s41598-025-09989-7
%U https://inrepo02.dkfz.de/record/303498